Penentuan Posisi Buah Catur Berbasis Hu Moments yang Dimodifikasi untuk Robot Pemain Catur dengan Sistem Tersemat

Authors

  • David Pang Universitas Sam Ratulangi
  • Glanny Mangindaan Universitas Sam Ratulangi

DOI:

https://doi.org/10.35799/tsj.v4i1.43340

Keywords:

pengenalan pola, Hu moment, catur, robot.

Abstract

Abstrak

Dalam situasi pandemi Covid-19, robot pemain catur bisa membantu para pemain catur untuk mendapatkan pengalaman bermain catur yang natural. Namun salah satu kesulitan terbesar pada robot pemain catur adalah bagaimana mengenali masing-masing buah catur pada papan catur menggunakan tangkapan kamera digital. Dalam riset ini kami mengusulkan metode pengenalan pola buah catur menggunakan Hu moment yang dimodifikasi untuk mengenali konfigurasi buah catur pada papan catur, untuk digunakan pada robot pemain catur dengan sistem tersemat. Meskipun metode ini sederhana dan punya beberapa keterbatasan, namun dapat menjalankan fungsinya dengan baik.

Kata kunci: pengenalan pola, Hu moment, catur, robot.

 

Abstract

In this pandemic situation of Covid-19, chess playing robot can help one to keep playing chess with a natural gaming experience. However, one of the biggest impediments to accomplishing a chess-playing robot is how to identify the chess game states from any images captured by a digital camera. In this research we propose a pattern recognition method based on modified Hu moment to recognize every chess piece on the chessboard, specifically to be applied to a chess-playing robot with an embedded system. Despite its simplicity and limitations, this method works well on purpose.

Keywords: pattern recognition, Hu moment, chess, robot.

References

Banerjee, N.; Saha, D.; Singh, A.; Sanyal, G. 2012. A Simple Autonomous Chess Playing Robot for Playing Chess against Any Opponent in Real Time. In proceeding of the International Conference on Computational Vision and Robotics; Institute for Project Management: Bhubaneshwar, India.

Chen, A.T.Y.; Wang, K.I.K.2016. Computer Vision Based Chess Playing Capabilities for the Baxter Humanoid Robot. In Proceedings of the International Conference on Control, Automation and Robotics, Hong Kong, China.

Chen, A.T.Y.; Wang, K.I.K. 2019. Robust Computer Vision Chess Analysis and Interaction with a Humanoid Robot. Computers 8(14).

Gonçalves, J.; Lima, J.; Leitão, P.2005. Chess Robot System : A Multi-Disciplinary Experience in Automation. In Proceeding of the Spanish Portuguese Congress on Electrical Engineering. Marbella, Spain.

Hu, Ming-Kuei. 1962. Visual pattern recognition by moment invariants. IRE transactions on information theory 8(2) : 179-187.

https://drive.google.com/drive/folders/1y1NW-R92kYZLka6vVgeAhgAwUkdKmu31?usp=sharing

Khan, R.A.M.; Kesavan, R.2014. Design and Development of Autonomous Chess Playing Robot. Int. J. Innov. Sci. Eng. Technol.1: 1–4.

Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton.2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25.

Mehta, A.; Mehta, H.2020. Augmented Reality Chess Analyzer (ARChessAnalyzer). J. Emerg. Investig. 2.

Xie, Y.; Tang, G.; Hoff, W. Chess Piece Recognition Using Oriented Chamfer Matching with a Comparison to CNN.2018. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision. Lake Tahoe, NV, USA.

Downloads

Published

2022-04-02

Issue

Section

Articles